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Salary
≈ $76k – $179k per year (Estimated)
Location
In office (Los Angeles)

Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on May 14, 2026. Furientis scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Furientis builds AI-driven autonomous defense systems for persistent awareness and rapid response across every domain.

The GN&C Team writes the laws of motion for our weapon system, and owns the vehicle those laws apply to. We design the airframe, build the plant model the rest of the company tests against, and write the guidance, navigation, and autopilot software that takes a launched interceptor from rail to intercept. The airframe sets the kinematic ceiling, the plant model decides what is real, and the GN&C software lives or dies on the truth of both. We do not split these concerns across organizations.

About the Role

    You will own the aerodynamic model and the vehicle outer mold line: body shape, control surfaces, static margin, and the pipeline that turns geometry into the coefficient tables plant and flight software consume. Hands-on, end-to-end: run and extend the empirical pipeline, calibrate against DATCOM and CFD, and plan wind-tunnel campaigns. For someone who has taken an airframe and its plant model from concept through wind tunnel and flight, ideally tactical, airborne, or missile-class.

What You'll Do

  • Own the vehicle outer mold line and the design trades on planform, sweep, taper, and area ratios against autopilot authority, static margin, and packaging.
  • Own the aero pipeline end-to-end: the geometry-to-coefficient-table workflow that feeds plant and flight software. Decide when to extend empirical methods versus go to CFD or wind tunnel.
  • Extend the nonlinear body-lift implementation (Allen-Perkins) and control surface buildups (Barrowman, Polhamus, Pitts-Nielsen carryover, Hemsch-Nielsen). Know which method is honest in which Mach and α regime.
  • Stand up the CFD pipeline from zero: tool selection, meshing strategy, turbulence model defaults, transonic and supersonic best practices, and validation discipline.
  • Calibrate against DATCOM near-term and wind-tunnel data long-term. Close the gaps the current pipeline shows against external cross-checks.
  • Plan and run wind-tunnel campaigns: test matrix, model and sting design, blockage corrections, Reynolds scaling, and data reduction back into runtime tables.
  • Own the static-margin envelope across burn and across Mach. Trade CG travel, fin sizing, and body length against intercept performance.
  • Work the 6-DOF plant model end-to-end: rigid-body dynamics, atmosphere, wind, aero, propulsion, mass-properties evolution, and sensor and actuator models. Decide where to invest fidelity and where to stub.
  • Build and extend the Monte Carlo dispersion harness: coefficient uncertainties, geometry sweeps, noise-source ablations, and the analysis to defend miss-distance claims.

Skills We're Hiring For

  • B.S. in Aerospace Engineering, Mechanical Engineering, Applied Math, Physics, or related. M.S. or Ph.D. preferred.
  • 7+ years of missile, munition, or high-speed airframe aerodynamics, with responsibility for one airframe from configuration through wind tunnel and flight.
  • Deep fluency with tactical-missile aero methods from first principles: slender-body, Allen-Perkins, Barrowman, Polhamus, Pitts-Nielsen, Hemsch-Nielsen. You can defend each at the whiteboard and know where it breaks.
  • Hands-on CFD across subsonic to supersonic. Meshing and solver fluency in at least one of Star-CCM+, OVERFLOW, Cart3D, SU2, or OpenFOAM.
  • Working fluency with Missile DATCOM and at least one engineering-level tool (AeroPredict, MISL3, RASAero, or equivalent). Reads, writes, and debugs input decks.
  • Built or owned a geometry-to-coefficient pipeline that feeds a real 6-DOF simulation, not a one-off study.
  • Wind-tunnel test experience: campaign planning, model and sting design, blockage corrections, Reynolds scaling, and data reduction back into a coefficient table.
  • Python (NumPy/SciPy) for pipeline scripting. Able to extend a coefficient-buildup codebase and stand up a Monte Carlo harness over aero parameters.
  • S&C derivative fluency: Cmα, Cmq, Cnβ, Cnr, Clp, control derivatives, hinge moments. Comfortable computing, measuring, and defending the difference.
  • Bias for low-cost airframes: track record of meeting kinematic and packaging requirements with a simpler shape or cheaper fabrication route than the legacy approach.
  • AI-native working style: daily use of agentic coding tools (Claude Code, Codex, OpenCode, or similar) for pipeline scripting, CFD post-processing, and analysis.

Bonus Points For

  • Direct flight experience on a tactical missile, munition, or interceptor airframe.
  • High-α and post-stall aerodynamics: vortex burst, induced roll, pitch-yaw cross-coupling, and the CFD or wind-tunnel work to characterize them.
  • Transonic aerodynamics: aerodynamic-center migration, shock-induced separation, and the wind-tunnel discipline required to trust transonic numbers.
  • Hinge-moment prediction and actuator-load characterization across the full Mach and α envelope.
  • Aero-optic effects: vortex-induced LOS noise on a strapdown seeker dome, boundary-layer distortion, and mitigation strategies.
  • Production transition: moved an airframe from prototype to volume manufacturing, including DFM-driven OML refinements.
  • Direct missile, munition, or interceptor program experience (ESSM, AMRAAM, Meteor, RAM, SM-class, or equivalent).
  • C++/C/Rust experience for pipeline tooling, geometry processing, or CFD pre/post. Forward-looking interest in moving parts of the aero stack to Rust.
  • Hobbyist drone, model-rocketry, or RC-aircraft build and flight experience that translates to packaging, surface tolerances, and field-handling intuition.
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